Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estima...

As an alternative to the conventional Hebb-type unsupervised learning, differential learning was studied in the domain of Hebb’s rule [1] and decorrelation [2]. In this paper we present an ICA algorithm which employs differential learning, thus named as differential ICA. We derive a differential ICA algorithm in the framework of maximum likelihood estimation and random walk model. Algorithm der...

optimization of the exploitation operation is one of the most important issues facing the mining engineers. since several technical and economic parameters depend on the cut-off grade, optimization of this parameter is of particular importance. the aim of this optimization is to maximize the net present value (npv). since the objective function of this problem is non-linear, three methods can b...

In this paper, the recently introduced optimisation strategy, imperialist competitive algorithm (ICA) is used to design an optimal antenna array which minimises the error probability for binary phase shift keying modulation, called minimum bit error rate (MBER) beamforming. ICA is used to deal with the high complexity and high dimensionality of this challenging problem which can not be easily s...

This paper addresses local stability analysis for the exible independent component analysis (ICA) algorithm [6] where the generalized Gaussian density model was employed for blind separation of mixtures of suband super-Gaussian sources. In the exible ICA algorithm, the shape of nonlinear function in the learning algorithm varies depending on the Gaussian exponent which is properly selected acco...

Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs.  In this paper, just-in-time (JIT) flow shop scheduling problem with preemp...

Independent Component Analysis (ICA) is a popular model for blind signal separation. The ICA model assumes that a number of independent source signals are linearly mixed to form the observed signals. We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gaussian noise. The main technical innovation of the algorithm is to use a fixed...

Frequency domain ICA has been used successfully to separate the utterances of interfering speakers in convolutive environments, see e.g. [6],[7]. Improved separation results can be obtained by applying a time frequency mask to the ICA outputs. After using the direction of arrival information for permutation correction, the time frequency mask is obtained with little computational effort. The pr...

—We present a new high-performance Convex Cauchy– Schwarz Divergence (CCS-DIV) measure for Independent Component Analysis (ICA) and Blind Source Separation (BSS). The CCS-DIV measure is developed by integrating convex functions into the Cauchy–Schwarz inequality. By including a convexity quality parameter, the measure has a broad control range of its convexity curvature. With this measure, a ne...

We propose an independent component analysis (ICA) algorithm which can separate mixtures of suband superGaussian source signals with self-adaptive nonlinearities. The ICA algorithem in the framework of natural Riemannian gradient is derived using the parameterized Weibull density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Weibull...